An Automatic Breast Tumor Detection and Classification including Automatic Tumor Volume Estimation Using Deep Learning Technique

OBJECTIVE: This study aims to develop automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning techniques based on computerized analysis of breast ultrasound images. When the skill levels of the radiologists and image quality are important to detect and diagnose the tumor using handheld ultrasound, the ability of this approach tends to assist the radiologist’s decision for breast cancer diagnosis. MATERIAL AND METHODS: Breast ultrasound images were provided by the Department of Radiology of Thammasat University and Queen Sirikit Center of Breast Cancer of Thailand. The dataset consists of 655 images including 445 benign and 210 malignant. Several data augmentation methods including blur, flip vertical, flip horizontal, and noise have been applied to increase the training and testing dataset. The tumor detection, localization, and classification were performed by drawing the appropriate bounding box around it using YOLO7 architecture based on deep learning techniques. Then, the automatic tumor volume estimation was performed using a simple pixel per metric technique. RESULT: The model demonstrated excellent tumor detection performance with a confidence score of 0.95. In addition, the model yielded satisfactory predictions on the test sets, with a lesion classification accuracy of 95.07%, a sensitivity of 94.97%, a specificity of 95.24%, a PPV of 97.42%, and an NPV of 90.91%. CONCLUSION: An automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning technique yielded satisfactory predictions in distinguishing benign from malignant breast lesions. In addition, automatic tumor volume estimation was performed. Our approach could be integrated into the conventional breast ultrasound machine to assist the radiologist’s decision for breast cancer diagnosis.